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Lund jet plane for Higgs tagging

by Charanjit K. Khosa

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Submission summary

Authors (as registered SciPost users): Charanjit Kaur Khosa
Submission information
Preprint Link: https://arxiv.org/abs/2110.15135v2  (pdf)
Date accepted: 2022-02-28
Date submitted: 2022-01-19 09:58
Submitted by: Khosa, Charanjit Kaur
Submitted to: SciPost Physics Proceedings
Proceedings issue: 50th International Symposium on Multiparticle Dynamics (ISMD2021)
Ontological classification
Academic field: Physics
Specialties:
  • High-Energy Physics - Phenomenology
Approaches: Computational, Phenomenological

Abstract

We study the boosted Higgs tagging using the Lund jet plane. The convolutional neural network is used for the Lund images data set to classify hadronically decaying Higgs from the QCD background. We consider $H\to b \bar{b}$ and $H \to gg$ decay for moderate and high Higgs transverse momentum and compare the performance with the cut based approach using the jet color ring observable. The approach using Lund plane images provides good tagging efficiency for all the cases.

Published as SciPost Phys. Proc. 10, 011 (2022)



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Resubmission 2110.15135v2 on 19 January 2022

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